{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# 逻辑回归\n",
    "\n",
    "**回归是如何变成分类的呢？**\n",
    "\n",
    "之前在线性回归问题中，得到了具体的回归值，如果此时任务要做一个二分类该怎么办呢？\n",
    "\n",
    "如果可以将连续的数值转换成对应的区间，这样就可以完成分类任务了，逻辑回归中借助 sigmoid 函数完成了数值映射，通过概率值比较来完成分类任务"
   ],
   "id": "e4593732702be416"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-19T10:27:23.763889Z",
     "start_time": "2025-03-19T10:27:22.577233Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "np.random.seed(18)"
   ],
   "id": "e05f6f40fa492615",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 神奇的 sigmoid 函数 (二分类)：",
   "id": "6eec1160ea66ab39"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-19T10:27:26.592841Z",
     "start_time": "2025-03-19T10:27:26.453365Z"
    }
   },
   "cell_type": "code",
   "source": [
    "t = np.linspace(-10, 10, 100)\n",
    "sigmoid = 1 / (1 + np.exp(-t))\n",
    "plt.figure(figsize=(9, 3))\n",
    "plt.plot([-10, 10], [0, 0], 'k:')\n",
    "plt.plot([-10, 10], [1, 1], 'k:')\n",
    "plt.plot([-10, 10], [0.5, 0.5], 'k:')\n",
    "plt.plot([0, 0], [0, 1], 'k:')\n",
    "plt.plot([-10, -10], [0, 1], 'k:')\n",
    "plt.plot([10, 10], [0, 1], 'k:')\n",
    "plt.plot(t, sigmoid, 'b-', linewidth=2)\n",
    "plt.xlabel('t')\n",
    "plt.title('Logistic function')"
   ],
   "id": "b8c1518d1c19f52b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Logistic function')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "**推导公式：**\n",
    "\n",
    "![](Logistic/1.png)"
   ],
   "id": "1ab5220fe7a4ef16"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "**鸢尾花数据集：**\n",
    "\n",
    "![](Logistic/2.png)"
   ],
   "id": "b748b543f0743d0a"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "**加载 sklearn 内置数据集**",
   "id": "e32d7289eca82f86"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-19T10:27:28.672694Z",
     "start_time": "2025-03-19T10:27:27.044846Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "print(iris.DESCR)"
   ],
   "id": "3aa5b53a4fba5611",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      ":Number of Instances: 150 (50 in each of three classes)\n",
      ":Number of Attributes: 4 numeric, predictive attributes and the class\n",
      ":Attribute Information:\n",
      "    - sepal length in cm\n",
      "    - sepal width in cm\n",
      "    - petal length in cm\n",
      "    - petal width in cm\n",
      "    - class:\n",
      "            - Iris-Setosa\n",
      "            - Iris-Versicolour\n",
      "            - Iris-Virginica\n",
      "\n",
      ":Summary Statistics:\n",
      "\n",
      "============== ==== ==== ======= ===== ====================\n",
      "                Min  Max   Mean    SD   Class Correlation\n",
      "============== ==== ==== ======= ===== ====================\n",
      "sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "============== ==== ==== ======= ===== ====================\n",
      "\n",
      ":Missing Attribute Values: None\n",
      ":Class Distribution: 33.3% for each of 3 classes.\n",
      ":Creator: R.A. Fisher\n",
      ":Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      ":Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. dropdown:: References\n",
      "\n",
      "  - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "    Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "    Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "  - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "    (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "  - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "    Structure and Classification Rule for Recognition in Partially Exposed\n",
      "    Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "    Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "  - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "    on Information Theory, May 1972, 431-433.\n",
      "  - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "    conceptual clustering system finds 3 classes in the data.\n",
      "  - Many, many more ...\n",
      "\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "***对于传统逻辑回归，要对标签进行变换，即属于当前类别为 1，其他类别为 0***",
   "id": "768d4cc870a69fff"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-19T10:27:28.685087Z",
     "start_time": "2025-03-19T10:27:28.674699Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X = iris['data'][:, 0:1]\n",
    "y = (iris['target'] == 2).astype(int)\n",
    "print(X.min())\n",
    "print(X.max())\n",
    "y"
   ],
   "id": "67b61731ded799bc",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.3\n",
      "7.9\n"
     ]
    },
    {
     "data": {
      "text/plain": [
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       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-19T10:27:28.871993Z",
     "start_time": "2025-03-19T10:27:28.691594Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X, y)"
   ],
   "id": "f64bfe2b11263e8e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ],
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-19T10:27:28.881515Z",
     "start_time": "2025-03-19T10:27:28.873510Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# reshape(-1, 1) 意思是自动计算行数，比较方便\n",
    "X_new = np.linspace(4.5, 8, 1000).reshape(-1, 1)\n",
    "y_proba = log_reg.predict_proba(X_new)\n",
    "y_proba[:10]"
   ],
   "id": "898e1bc22e5e3772",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.98315041, 0.01684959],\n",
       "       [0.9830205 , 0.0169795 ],\n",
       "       [0.98288961, 0.01711039],\n",
       "       [0.98275773, 0.01724227],\n",
       "       [0.98262486, 0.01737514],\n",
       "       [0.98249097, 0.01750903],\n",
       "       [0.98235607, 0.01764393],\n",
       "       [0.98222016, 0.01777984],\n",
       "       [0.98208321, 0.01791679],\n",
       "       [0.98194523, 0.01805477]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "***随着输入特征数值的变化，结果概率值也会随之变化***",
   "id": "97f6f9d6476ff65f"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-19T10:27:29.010201Z",
     "start_time": "2025-03-19T10:27:28.882210Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot(X_new, y_proba[:, 0], 'g-', label='Not Iris-Virginica')\n",
    "plt.plot(X_new, y_proba[:, 1], 'r-', label='Iris-Virginica')\n",
    "plt.plot([5, 7.5], [0.5, 0.5], 'k:')\n",
    "boundary = X_new[y_proba[:, 1] >= 0.5][0][0]\n",
    "plt.plot([boundary, boundary], [-0.1, 1.1], 'k:', linewidth=2)\n",
    "plt.arrow(boundary - 0.1, 1, -0.3, 0, head_width=0.05, head_length=0.1, color='g')\n",
    "plt.arrow(boundary + 0.1, 1, 0.3, 0, head_width=0.05, head_length=0.1, color='r')\n",
    "plt.text(boundary + 0.03, 0, 'Decision   Boundary', ha='center', fontsize=14)\n",
    "plt.text(7.55, 0.48, '0.5')\n",
    "plt.xlabel('Sepal length (cm)', fontsize=12)\n",
    "plt.ylabel('Odds', fontsize=12)\n",
    "plt.legend(loc='center left')\n",
    "plt.title('Type Judgment to Sepal Length', fontsize=14)"
   ],
   "id": "fec21c185df3a35a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Type Judgment to Sepal Length')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-19T10:27:29.023068Z",
     "start_time": "2025-03-19T10:27:29.011718Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X = iris['data'][:, 2:]\n",
    "y = (iris['target'] == 2).astype(int)\n",
    "X[:10]"
   ],
   "id": "a4b66c8bc33a11c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.4, 0.2],\n",
       "       [1.4, 0.2],\n",
       "       [1.3, 0.2],\n",
       "       [1.5, 0.2],\n",
       "       [1.4, 0.2],\n",
       "       [1.7, 0.4],\n",
       "       [1.4, 0.3],\n",
       "       [1.5, 0.2],\n",
       "       [1.4, 0.2],\n",
       "       [1.5, 0.1]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-19T10:27:29.037030Z",
     "start_time": "2025-03-19T10:27:29.024073Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X, y)"
   ],
   "id": "6368359bf7b8688",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ],
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "***决策边界的绘制***\n",
    "- 构建坐标数据，合理的范围当中，根据实际训练时输入数据来决定\n",
    "- 整合坐标点，得到所有测试输入数据坐标点\n",
    "- 预测，得到所有点的概率值\n",
    "- 绘制等高线，完成决策边界"
   ],
   "id": "b5acc6da1a2d6521"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "构建坐标数据：\n",
    "\n",
    "1.0  6.9\n",
    "\n",
    "0.1  2.5"
   ],
   "id": "be0da5a49e1ed620"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-19T10:27:31.329656Z",
     "start_time": "2025-03-19T10:27:31.317991Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# meshgrid() 的作用是创建二维或三维的网格坐标矩阵 (笛卡尔积)\n",
    "x0, x1 = np.meshgrid(np.linspace(1, 7, 500).reshape(-1, 1), np.linspace(0, 2.5, 200).reshape(-1, 1))\n",
    "# ravel() 返回的是视图（view），而 flatten() 返回的是副本（copy）\n",
    "# 修改视图会影响原数组，而副本是独立的内存，修改不会影响原数组。\n",
    "X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "X_new.shape"
   ],
   "id": "c3828e450e5c2762",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-19T10:27:31.893826Z",
     "start_time": "2025-03-19T10:27:31.878961Z"
    }
   },
   "cell_type": "code",
   "source": [
    "y_proba = log_reg.predict_proba(X_new)\n",
    "y_proba.shape"
   ],
   "id": "bc6480853401a0cf",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-19T10:27:32.772586Z",
     "start_time": "2025-03-19T10:27:32.531473Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(X[y == 0, 0], X[y == 0, 1], 'bs')\n",
    "plt.plot(X[y == 1, 0], X[y == 1, 1], 'g^')\n",
    "\n",
    "z = y_proba[:, 1].reshape(x0.shape)\n",
    "# cmap=plt.cm.brg 用于设置颜色映射的参数\n",
    "contour = plt.contour(x0, x1, z, cmap=plt.cm.brg)\n",
    "# plt.clabel() 是用于给等高线图添加标签的函数，通常与 contour() 函数配合使用。\n",
    "plt.clabel(contour)\n",
    "plt.axis((2.9, 7, 0.9, 2.5))\n",
    "plt.text(3.1, 1.5, 'Not Iris-Virginica', fontsize=14, color='b')\n",
    "plt.text(6.3, 1.6, 'Iris-Virginica', fontsize=14, color='g')"
   ],
   "id": "74161fdec14013cb",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(6.3, 1.6, 'Iris-Virginica')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "**如何进行多类别分类呢？--> Softmax**\n",
    "\n",
    "![](Logistic/3.png)"
   ],
   "id": "dd69963ac9b1eea5"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-19T10:27:34.498736Z",
     "start_time": "2025-03-19T10:27:34.495310Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X = iris['data'][:, 2:]\n",
    "y = iris['target']"
   ],
   "id": "ace7f402ba7ab4e",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-19T10:31:59.461299Z",
     "start_time": "2025-03-19T10:31:59.452370Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# TODO LogisticRegression() 会自动根据标签判断分类\n",
    "softmax_reg = LogisticRegression()\n",
    "softmax_reg.fit(X, y)"
   ],
   "id": "5915b8f321f24532",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ],
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-19T10:51:45.397156Z",
     "start_time": "2025-03-19T10:51:45.251248Z"
    }
   },
   "cell_type": "code",
   "source": [
    "x0, x1 = np.meshgrid(np.linspace(0.5, 8, 500).reshape(-1, 1), np.linspace(0, 3, 200).reshape(-1, 1))\n",
    "X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "y_proba = softmax_reg.predict_proba(X_new)\n",
    "y_predict = softmax_reg.predict(X_new)\n",
    "\n",
    "z_proba = y_proba[:, 1].reshape(x0.shape)\n",
    "z = y_predict.reshape(x0.shape)\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(X[y == 0, 0], X[y == 0, 1], 'yo', label='Iris-Setosa')\n",
    "plt.plot(X[y == 1, 0], X[y == 1, 1], 'bs', label='Iris-Versicolour')\n",
    "plt.plot(X[y == 2, 0], X[y == 2, 1], 'g^', label='Iris-Virginica')\n",
    "\n",
    "# contourf() 会根据数据的高度值（Z）自动填充不同区域的颜色，通过颜色深浅直观展示数据分布。\n",
    "# from matplotlib.colors import ListedColormap\n",
    "# custom_cmap = ListedColormap(['#FFFD55', '#3282F6', '#A1FB8E'])\n",
    "# plt.contourf(x0, x1, z, cmap=custom_cmap)\n",
    "\n",
    "contour = plt.contour(x0, x1, z_proba, cmap=plt.cm.brg)\n",
    "plt.clabel(contour)\n",
    "plt.xlabel('Petal length', fontsize=12)\n",
    "plt.ylabel('Petal width', fontsize=12)\n",
    "plt.legend(loc='center left')"
   ],
   "id": "1ecb9da91baa9b94",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11fb3b5c2d0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 36
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
